An agent-based simulation model to evaluate alternative co-payment scenarios for contributing to health systems financing Michele Sonnessa 1 , Elena Ta `nfani 1 * and Angela Testi 1 1 Department of Economics and Business Studies, University of Genova, Via Vivaldi 5, 16126 Genova, Italy Ageing populations, rapid technological progress and recent public budget cuts currently threaten the sustainability of public health systems. To meet growing needs with declining resources, decision-makers must identify new ways to avoid reducing the quality of services offered to citizens. This paper focuses on the so-called ‘‘co-payment’’ tools aimed to obtain additional resources for the public health budget directly from citizens. Whereas certain forms of co-payments have always been introduced within health systems to prevent moral hazard behaviours, other co-payment mechanisms are explicitly intended to help finance public healthcare systems. Literature and empirical findings do not agree about the final impact of such co-payment tools, particularly whether they can attain system sustainability and guarantee equitably delivered services. In this paper, we develop an agent-based simulation model which can be used by decision-makers as a decision support tool to compare different co-payment rules and evaluate their impact on the public budget and the health expense of different groups of citizens. Journal of the Operational Research Society (2017) 68(5), 591–604. doi:10.1057/s41274-016-0022-5; published online 1 December 2016 Keywords: health economics; agent-based simulation; decision support system; health system financing 1. Introduction The paper considers co-payment healthcare systems, whereby individuals seeking service may be required to contribute towards the costs. Such systems are mainly intended to reduce consumption and the burden on the healthcare system, although it appears that their effectiveness in reducing expenditure and equity is not clearly proven. The research literature is divided over the co-payment benefits and pitfalls (Carrieri, 2010; Manning et al, 1987). Indeed, predicting how different co-payment systems impact the public budget and the health expense of different citizen groups is quite difficult due to the unpredictable interaction among different effects. As patients perceive co-payment as a price increase, we can predict that price increases reduce the demand, but the entity of such reduction relies on health services elasticity. Following traditional microeconomics findings, we know that elasticity depends on the price effect, which is the sum of substitution and income effects. The former effect depends on how many substitutes of a given good exist. The latter effect depends on how high the income is in absolute terms and how high health expenses are in relative terms (with respect to other goods). If the aim is to finance health services, co-payments should be applied to services with rigid demand, i.e. no substitution effect exists and annual health expenses are high (e.g. health services for patient with chronic conditions), thus contradicting the equity principle. In summary, before introducing co-payment instruments in universal health care systems, decision-makers should know and evaluate in advance: (i) whether increasing co-payments will reduce patient demand for services or produce no consistent fiscal return; (ii) which is the best exemption structure to correct excessive payments by chronic or deprived patients. In this general context, this paper develops an agent-based simulation model to evaluate the impact of different co- payment rules in a particular Italian area. More specifically, the main aim is to investigate the effect of replacing the current co-payment rules, mainly based on the prices of health services, with a deductible rule based on patient income. As far as we know, such a modelling framework has not been already developed by the literature with the same level of disaggregation that is able to capture and evaluate detailed differences between specific citizen groups. The remainder of the paper is as follows. In Section 2, the problem addressed is described together with the literature review. In Section 3 the agent-based simulation model is described, while in Section 4 the data collection and analysis and model parameterisation to be used in the agent’s decision algorithms are reported. Section 5 gives some details on model *Correspondence: Elena Ta `nfani, Department of Economics and Business Studies, University of Genova, Via Vivaldi 5, 16126 Genova, Italy. E-mail: etanfani@economia.unige.it Journal of the Operational Research Society (2017) 68, 591–604 ª 2016 The Operational Research Society. All rights reserved. 0160-5682/17 www.palgrave.com/journals